Introduction to the special issue on mining and learning with graphs

Abstract

Driven by application areas ranging from biology to the World Wide Web, research in Data Mining and Machine Learning is nowadays increasingly focusing on the analysis of structured data. Of particular interest are data that consist of interrelated parts or data that are characterized by collections of interrelated objects, linked together into complex graphs and structures. Dealing with such interrelated data is one of the major research challenges that we are facing. The aim of this special issue is to bring together papers from different sub-disciplines within Machine Learning and Data Mining that focus on the analysis of structured data. This special issue was first conceived as a forum for publishing extended versions of papers presented at the annual workshop on Machine Learning and Graphs (MLG) 2008 colocated with the International Conference on Machine Learning (ICML) 2008 in Helsinki, Finland. Later, the scope was extended and we solicited relevant papers from researchers in all areas of machine learning and data mining, working on mining and learning with graphs